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trEPR documentation

Welcome! This is the documentation for trEPR, a Python package for processing and analysis of time-resolved electron paramagnetic resonance (tr-EPR) spectra based on the ASpecD framework. For general information see its Homepage. Due to the inheritance from the ASpecD framework, all data generated with the trepr package are completely reproducible and have a complete history.

What is even better: Actual data processing and analysis no longer requires programming skills, but is as simple as writing a text file summarising all the steps you want to have been performed on your dataset(s) in an organised way. Curious? Have a look at the following example:

format:
  type: ASpecD recipe
  version: '0.2'

settings:
  default_package: trepr

datasets:
  - /path/to/first/dataset
  - /path/to/second/dataset

tasks:
  - kind: processing
    type: PretriggerOffsetCompensation
  - kind: processing
    type: BackgroundCorrection
    properties:
      parameters:
        num_profiles: [10, 10]
  - kind: singleplot
    type: SinglePlotter2D
    properties:
      filename:
        - first-dataset.pdf
        - second-dataset.pdf

Interested in more real-live examples? Check out the use cases section and the growing list of examples providing complete recipes for different needs.

Features

A list of features:

• Fully reproducible processing of tr-EPR data

• Import and export of data from and to different formats

• Customisable plots

• Automatically generated reports

• Recipe-driven data analysis, allowing tasks to be performed fully unattended in the background and without programming skills

And to make it even more convenient for users and future-proof:

• Open source project written in Python (>= 3.7)

• Extensive user and API documentation

Warning

The trepr package is currently under active development and still considered in Beta development state. Therefore, expect frequent changes in features and public APIs that may break your own code. Nevertheless, feedback as well as feature requests are highly welcome.

Requirements

The trepr package comes with a rather minimal set of requirements:

• Python >= 3.7 with aspecd, numpy, scipy and matplotlib packages

• command-line access for recipe-driven data analysis

• metadata (in addition to the usual parameter files)

• EPR data in readable formats (details in the trepr.io module)

How to cite

trepr is free software. However, if you use trepr for your own research, please cite it appropriately:

Jara Popp, Mirjam Schröder, Till Biskup. trepr (2021). doi:10.5281/zenodo.4897112

To make things easier, trepr has a DOI provided by Zenodo, and you may click on the badge below to directly access the record associated with it. Note that this DOI refers to the package as such and always forwards to the most current version.

Where to start

Users new to the trepr package should probably start at the beginning, those familiar with its underlying concepts may jump straight to the section explaining frequent use cases.

The API documentation is the definite source of information for developers, besides having a look at the source code.

Installation

To install the trepr package on your computer (sensibly within a Python virtual environment), open a terminal (activate your virtual environment), and type in the following:

pip install trepr

Have a look at the more detailed installation instructions as well.

Related projects

There is a number of related packages users of the trepr package may well be interested in, as they have a similar scope, focussing on spectroscopy and reproducible research.

• ASpecD

  A Python framework for the analysis of spectroscopic data focussing on reproducibility and good scientific practice. The framework the trepr package is based on, developed by T. Biskup.

• cwepr

  Package for processing and analysing continuous-wave electron paramagnetic resonance (cw-EPR) data, originally implemented by P. Kirchner, developed and maintained by M. Schröder and T. Biskup.

• FitPy

  Framework for the advanced fitting of models to spectroscopic data focussing on reproducibility, developed by T. Biskup.

You may as well be interested in the LabInform project focussing on the necessary more global infrastructure in a laboratory/scientific workgroup interested in more reproducible research. In short, LabInform is “The Open-Source Laboratory Information System”.

Finally, don’t forget to check out the website on reproducible research covering in more general terms aspects of reproducible research and good scientific practice.

License

This program is free software: you can redistribute it and/or modify it under the terms of the BSD License. However, if you use the trepr package for your own research, please cite it appropriately. See How to cite for details.

A note on the logo

The snake (a python) resembles the lines of a tr-EPR spectrum, most probably a light-induced spin-polarised triplet state. The copyright of the logo belongs to J. Popp.